• Annual Conference of KIPS
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• 2013.05a
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• pp.248-249
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• 2013

This study presents a potential field method for path planning to goal with a mobile robot in unknown environment. The proposed algorithm allows mobile robot to navigate through static obstacles, and find the path in order to reach the target without collision. This algorithm provides the robot with the possibility to move from the initial position to the final position (target). Stage and Player simulator is used to perform the robot motion and implement the potential field algorithm in C/C++ for performance evaluation. Two-dimensional terrain model is used to simulate the ability of robot in motion planning without any collision.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5624-5638
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• 2016

A novel method based on Surf points is proposed to detect and lock-track single ground target in aerial videos. Videos captured by moving cameras contain complex motions, which bring difficulty in moving object detection. Our approach contains three parts: moving target template detection, search area estimation and target tracking. Global motion estimation and compensation are first made by grids-sampling Surf points selecting and matching. And then, the single ground target is detected by joint spatial-temporal information processing. The temporal process is made by calculating difference between compensated reference and current image and the spatial process is implementing morphological operations and adaptive binarization. The second part improves KALMAN filter with surf points scale information to predict target position and search area adaptively. Lastly, the local Surf points of target template are matched in this search region to realize target tracking. The long-term tracking is updated following target scaling, occlusion and large deformation. Experimental results show that the algorithm can correctly detect small moving target in dynamic scenes with complex motions. It is robust to vehicle dithering and target scale changing, rotation, especially partial occlusion or temporal complete occlusion. Comparing with traditional algorithms, our method enables real time operation, processing $520{\times}390$ frames at around 15fps.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.148-155
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• 2015

The Levenberg-Marquardt method is a well known solution about the least square problem. However, in a Target Motion Analysis(TMA) application most of researches have used the Gauss-Newton method as a batch estimator, which of inverse matrix calculation may causes instability problem. In this paper, Levenberg-Marquardt method is applied to TMA problem to prevent its divergence. In experiment, its performance is compared with Gauss-Newton in domain of range, course and speed. Monte Carlo simulation reveals the convergence time and reliability of the TMA based on Levenberg-Marquardt.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.938-958
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• 2024

Unsupervised Video Object Segmentation (UVOS) is a highly challenging problem in computer vision as the annotation of the target object in the testing video is unknown at all. The main difficulty is to effectively handle the complicated and changeable motion state of the target object and the confusion of similar background objects in video sequence. In this paper, we propose a novel deep Dual-stream Co-enhanced Network (DC-Net) for UVOS via bidirectional motion cues refinement and multi-level feature aggregation, which can fully take advantage of motion cues and effectively integrate different level features to produce high-quality segmentation mask. DC-Net is a dual-stream architecture where the two streams are co-enhanced by each other. One is a motion stream with a Motion-cues Refine Module (MRM), which learns from bidirectional optical flow images and produces fine-grained and complete distinctive motion saliency map, and the other is an appearance stream with a Multi-level Feature Aggregation Module (MFAM) and a Context Attention Module (CAM) which are designed to integrate the different level features effectively. Specifically, the motion saliency map obtained by the motion stream is fused with each stage of the decoder in the appearance stream to improve the segmentation, and in turn the segmentation loss in the appearance stream feeds back into the motion stream to enhance the motion refinement. Experimental results on three datasets (Davis2016, VideoSD, SegTrack-v2) demonstrate that DC-Net has achieved comparable results with some state-of-the-art methods.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.635-638
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• 1998

This paper proposes a simple method to measure system's performance in target tracking problems. Essentially employing the Cramer-Rao lower bound (CRLB) on trakcing accuracy, an algorithm of predicting system's performance under various scenarios is developed. The input data is a collection of measurements over time fromsensors embedded in gaussian noise. The target of interest may not maneuver over the processing time interval while the own ship observing platform may maneuver in an arbitrary fashion. Th eproposed approach is demonstrated and discussed through simulation results.

• The Journal of the Acoustical Society of Korea
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• v.38 no.4
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• pp.387-395
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• 2019

TMA (Target Motion Analysis) in passive sonar is generally conducted with the bearing only or the bearing frequency. In order to conduct TMA fast and accurately, it is essential to estimate a initial target maneuver precisely. The accuracy of TMA can be improved by using SNR (Signal to Noise Ratio) information and acoustic propagation model additionally. This method assumes that the radiated noise level of the target is known, but the accuracy of TMA can be degraded due to a mismatch between the assumed radiated noise level and the actual radiated noise level. In this paper, TMA with the acoustic propagation model, bearing measurements, and SNR information is conducted in the ocean environment of South Korea (East Sea/ Yellow Sea/ South Sea). And the performance analysis of TMA for the mismatch in the radiated noise is presented.

• 전자공학회논문지 IE
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• v.43 no.2
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• pp.54-62
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• 2006

In this paper, a new stereo object tracking system using the disparity motion vector is proposed. In the proposed method, the time-sequential disparity motion vector can be estimated from the disparity vectors which are extracted from the sequence of the stereo input image pair and then using these disparity motion vectors, the area where the target object is located and its location coordinate are detected from the input stereo image. Basing on this location data of the target object, the pan/tilt embedded in the stereo camera system can be controlled and as a result, stereo tracking of the target object can be possible. From some experiments with the 2 frames of the stereo image pairs having $256\times256$ pixels, it is shown that the proposed stereo tracking system can adaptively track the target object with a low error ratio of about 3.05 % on average between the detected and actual location coordinates of the target object.

• Progress in Medical Physics
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• v.23 no.3
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• pp.145-153
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• 2012

In this study, we quantify the residual motion artifact in 4D-CT scan using the dynamic lung phantom which could simulate respiratory target motion and suggest a simple one-dimension theoretical model to explain and characterize the source of motion artifacts in 4DCT scanning. We set-up regular 1D sine motion and adjusted three level of amplitude (10, 20, 30 mm) with fixed period (4s). The 4DCT scans are acquired in helical mode and phase information provided by the belt type respiratory monitoring system. The images were sorted into ten phase bins ranging from 0% to 90%. The reconstructed images were subsequently imported into the Treatment Planning System (CorePLAN, SC&J) for target delineation using a fixed contour window and dimensions of the three targets are measured along the direction of motion. Target dimension of each phase image have same changing trend. The error is minimum at 50% phase in all case (10, 20, 30 mm) and we found that ${\Delta}S$ (target dimension change) of 10, 20 and 30 mm amplitude were 0 (0%), 0.1 (5%), 0.1 (5%) cm respectively compare to the static image of target diameter (2 cm). while the error is maximum at 30% and 80% phase ${\Delta}S$ of 10, 20 and 30 mm amplitude were 0.2 (10%), 0.7 (35%), 0.9 (45%) cm respectively. Based on these result, we try to analysis the residual motion artifact in 4D-CT scan using a simple one-dimension theoretical model and also we developed a simulation program. Our results explain the effect of residual motion on each phase target displacement and also shown that residual motion artifact was affected that the target velocity at each phase. In this study, we focus on provides a more intuitive understanding about the residual motion artifact and try to explain the relationship motion parameters of the scanner, treatment couch and tumor. In conclusion, our results could help to decide the appropriate reconstruction phase and CT parameters which reduce the residual motion artifact in 4DCT.

• Annual Conference of KIPS
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• 2012.11a
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• pp.552-555
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• 2012

The main goal of this paper is to develop a robust visual tracking algorithm with particle filtering. Visual Tracking with particle filter technique is not easy task due to cluttered environment, illumination changes. To deal with these problems, we develop an efficient observation model for target tracking with particle filter. We develop a robust phase correlation combined with motion information based observation model for particle filter framework. Phase correlation provides straight-forward estimation of rigid translational motion between two images, which is based on the well-known Fourier shift property. Phase correlation has the advantage that it is not affected by any intensity or contrast differences between two images. On the other hand, motion cue is also very well known technique and widely used due to its simplicity. Therefore, we apply the phase correlation integrated with motion information in particle filter framework for robust tracking. In experimental results, we show that tracking with multiple cues based model provides more reliable performance than single cue.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.6
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• pp.62-69
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• 2002

For improving the motion accuracy of hydrostatic table, corrective machining algorithm is proposed in this paper. The algorithm consists of three main processes. reverse analysis is performed firstly to estimate rail profile from measured linear and angular motion error, in the algorithm. For the next step, corrective machining information is decided as referring to the estimating rail profile. Finally, motion errors on correctively machined rail are analized by using motion error analysis method proposed in the previous paper. These processes can be iterated until the analized motion errors are satisfied with target accuracy. In order to verify the validity of the algorithm theoretically, motion errors by the estimated rail, after corrective machining, are compared with motion errors by true rail assumed as the measured value. Estimated motion errors show good agreement with assumed values, and it is confirmed that the algorithm is effective to acquire the corrective machining information to improve the accuracy of hydrostatic table.